arXiv:2606.25246v1 Announce Type: cross Abstract: Vision Language Models (VLMs) have shown promising capabilities in medical image analysis by jointly understanding visual and textual information for tasks such as Visual Question Answering. However, existing hematology vision-language resources remain predominantly English centric, limiting their applicability in multilingual healthcare environments. This challenge is releveant generally to South Asia and specifically to Pakistan, where Urdu is widely used despite healthcare information and digital medical systems being largely dependent on En
The proliferation of Vision Language Models (VLMs) and the increasing need for their application in diverse, multilingual healthcare settings, particularly in underserved regions, is driving this development.
This initiative addresses a critical gap in medical AI, broadening its utility beyond English-centric systems and enabling more equitable access to advanced diagnostic tools globally.
The availability of multilingual hematology datasets will allow for the development of more inclusive and globally applicable medical AI products, reducing the dependency on English-only models.
- · Multilingual AI developers
- · Healthcare providers in non-English speaking regions
- · Patients in diverse linguistic environments
- · South Asian healthcare technology sector
- · Monolingual AI software providers
- · Healthcare systems reliant solely on English-based AI
Improved diagnostic accuracy and accessibility for hematological conditions in multilingual contexts.
Accelerated development of other multilingual medical AI applications beyond hematology, fostering a more inclusive global AI healthcare ecosystem.
Potential for new AI-driven healthcare economic zones to emerge in regions historically underserved by Western-centric medical technology.
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